employment

Feudal Future Podcast: The State of the Economy

Joel Kotkin and Marshall Toplansky — Wed, 07 Sep 2022 16:34:20 -0400

On this episode of Feudal Future, hosts Joel Kotkin and Marshall Toplansky are joined by President Emeritus of Chapman University, Jim Doti, to discuss the state of the US economy and the recession.

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Related Report:

From Chapman’s Center of Demographics & Policy, Joel Kotkin & Marshall Toplansky co-author the brand new report on restoring The California Dream.

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This show is presented by the Chapman Center for Demographics and Policy, which focuses on research and analysis of global, national and regional demographic trends and explores policies that might produce favorable demographic results over time.

43% of Canada’s Employed Worked Majority of Hours at Home: January 2022

Wendell Cox — Mon, 21 Mar 2022 22:58:48 -0400

Statistics Canada reports that remote work reached a pandemic era recently.

“Since the onset of the pandemic, the Labour Force Survey has been tracking the proportion of non-absent workers who worked from home. During the week of January 9 to 15, more than 4 in 10 (43.0%) of those employed worked most of their hours from home, the highest proportion since the initial lockdown in April 2020. Among those who did not usually work any of their hours at home, 30.3% worked at home for at least part of the week.”

This was included in the Statistics Canada January monthly bulletin on urban transit ridership.

Wendell Cox is principal of Demographia, an international public policy firm located in the St. Louis metropolitan area. He is a founding senior fellow at the Urban Reform Institute, Houston, a Senior Fellow with the Frontier Centre for Public Policy in Winnipeg and a member of the Advisory Board of the Center for Demographics and Policy at Chapman University in Orange, California. He has served as a visiting professor at the Conservatoire National des Arts et Metiers in Paris. His principal interests are economics, poverty alleviation, demographics, urban policy and transport. He is co-author of the annual Demographia International Housing Affordability Survey and author of Demographia World Urban Areas.

Mayor Tom Bradley appointed him to three terms on the Los Angeles County Transportation Commission (1977-1985) and Speaker of the House Newt Gingrich appointed him to the Amtrak Reform Council, to complete the unexpired term of New Jersey Governor Christine Todd Whitman (1999-2002). He is author of War on the Dream: How Anti-Sprawl Policy Threatens the Quality of Life and Toward More Prosperous Cities: A Framing Essay on Urban Areas, Transport, Planning and the Dimensions of Sustainability.

UK Economy Survives Pandemic: Government Assistance and Remote Work the Reasons

Wendell Cox — Thu, 08 Jul 2021 20:28:58 -0400

In an article entitled “Financial hit from Covid far less drastic than feared,” The Times of London reported on July 8 reported that “Unemployment, debt and earnings have not worsened significantly as a result of the pandemic, Britain’s leading economic think tank has concluded, hailing the findings as “astonishing”.

The report by the Institute of Fiscal Studies (IFS) expressed “surprise” that unemployment is much lower than was expected, that there has been only a small increase in people in arrears on bills and that the number of people using food banks increased minimally during the pandemic and is now below pre-pandemic levels.

At the same time, IFS noted “while the national picture was remarkable, its figures masked groups who have seen clear increases in hardship, particularly the self-employed and families already experiencing in-work poverty before the pandemic hit.”

The article attributed the positive developments to government interventions to support working people, more than one-third of whom received government support during the pandemic.

We add that, had it not been for the conversion to remote work, these results could not have been achieved. According to the OECD (See Note below), in Mid April of 2020, 49% of the United Kingdom pre-pandemic work force was working remotely, while 20% were working at their physical employment locations, leaving 31% who were not working (Figure). This data suggests that about 70% of the working population was teleworking (Figure). Had remote work not been embraced, the unemployment rate would have been far higher and the economic disruption would likely have been at least as bad as the most dire expectations.

Note: OECD, “Working during COVID-19: Cross-country evidence from real-time survey data.” At oecd-ilibrary.org

Virtual Town Hall: Revitalizing California's Business Climate

New Geography — Fri, 08 Jan 2021 20:12:12 -0500

You are invited to join Chapman University’s Vice President of Research Thomas Piechota who will host the next Ask the Experts Town Hall on Friday, January 22, from 11 – 12:30 P.M. (PST). The installment this month will be moderated by Dean Thomas Turk of the Argyros School of Business and Economics who will be joined by Joel Kotkin, Presidential Fellow in Urban Futures at Chapman University and Marshall Toplansky, Clinical Assistant Professor of Management Science at Chapman University. Together, Chapman faculty will be joined by local community experts: Lucy Dunn, President and CEO, Orange County Business Council; Tracy Hernandez, CEO, Los Angeles County Business Federation; Jim Wunderman, President and CEO, Bay Area Council; and Rob Lapsley, President, California Business Roundtable.

Alongside Chapman faculty, our distinguished guests will discuss how best California’s business climate can be revitalized to avoid the loss of companies, higher unemployment rates, and poverty. They will also explore how businesses can be key to correcting these problems including encouraging government to listen more to the challenges being faced.

The entire Chapman community (staff, faculty, and students) and the broader community are welcome to join. Register for the webinar here, or visit the Ask the Experts Website.

For more information visit blogs.chapman.edu/

Guest Experts

Joel Kotkin, Presidential Fellow in Urban Futures, Chapman University

Marshall Toplansky, Clinical Assistant Professor of Management Science at Chapman University

Lucy Dunn, President and CEO, Orange County Business Council

Tracy Hernandez, CEO Los Angeles County Business Federation, Los Angeles

Jim Wunderman, President and CEO, Bay Area Council

Rob Lapsley, President, California Business Roundtable

Downtown Employment Estimates

Aaron M. Renn — Wed, 14 Oct 2020 11:00:13 -0400

Robert Manduca, who made an extremely detailed map of job locations in the US back in 2014, recently published a paper with estimates for central business district employment for US metropolitan and micropolitan areas.

Manduca utilizes an algorithm to delineate CBDs as areas of contiguous job density. This creates a standardized calculation of downtowns that allows comparisons across regions. This is very useful because there is no standardized definition of downtowns.

You can download a spreadsheet with summary information about every MSA. In some cases, the largest employment area is suburban, so you’ll see the CBD classified as “suburb” in this case. (The spreadsheet is of the largest employment center in each metro).

Read the rest at Heartland Intelligence.

3 Reasons COVID-19 is Creating a Rural Employment Crisis

Aidan Calvelli — Tue, 05 May 2020 13:01:50 -0400

The COVID-19 pandemic is creating a health crisis unlike anything we’ve ever seen. But the disease’s damage doesn’t stop there. With millions of people forced to stay at home, the economy has cratered. 701,000 people lost their job in March alone—the worst month for American jobs since the Great Recession in March 2009.

At CORI, we knew there was an economic crisis happening in rural America even before this crash. Small town industries have been declining, tech jobs have boomed only in cities, and young people have left their rural hometowns in search of opportunity. And without good access to broadband, rural areas haven’t been able to tap into the digital economy’s growth.

All those existing challenges made us worried about what could happen when a COVID-related recession hit. Led by our in-house economist Mark Rembert, we created an Employment Risk Index, which ranks counties by how vulnerable they are to losing jobs based on three factors relevant to the COVID crisis: employment in high-risk industries, employment in small businesses, and age of workforce. What we found was troubling: Rural America faces a disproportionately high employment risk.

20th century industries are hardest hit

Our analysis found four industries at highest risk from COVID-19, whether through decreased demand or an inability to employ remote work: tourism, manufacturing, transportation, and natural resource production. In metro areas, these industries make up just 43 percent of all jobs. In rural areas, they represent an astonishing 56 percent.

View the geographical data and read the rest of this article at Center on Rural Innovation (CORI).

Reprinted with permission from American Geographical Society ags@americangeo.org

Aidan Calvelli is Research and Communications Analyst at CORI. He has edited books and articles about democracy and the presidency that have been featured by national news outlets and publishers. Aidan grew up in Rochester, NY and Shelburne, VT, and holds an A.B. in Political Theory from Brown University.

The 2012 Year in Unemployment

Aaron M. Renn — Tue, 07 May 2013 12:04:10 -0400

I recently looked at the changes in jobs in metro areas for 2012. Here’s a follow-on look at unemployment. First a look at the national unemployment rate picture, which has improved remarkably.

2012 Unemployment Rate by County

To put this in perspective, here’s the corresponding map for 2009:

2009 Unemployment Rate by County

It’s interesting to see where there has been improvement versus where there hasn’t, though I stop thresholding at 10% so that if people we well above it but dropped to just merely above it, my maps wouldn’t show that improvement.

Here’s a look at the large metro areas, ranked by total decline in unemployment rate.

Rank by Total Improvement	Metro Area	2011	2012	Total Change
1	Las Vegas-Paradise, NV	13.5	11.2	-2.3
2	Orlando-Kissimmee-Sanford, FL	10.2	8.4	-1.8
3	Tampa-St. Petersburg-Clearwater, FL	10.6	8.8	-1.8
4	Miami-Fort Lauderdale-Pompano Beach, FL	10.2	8.5	-1.7
5	Jacksonville, FL	9.9	8.3	-1.6
6	Sacramento–Arden-Arcade–Roseville, CA	11.9	10.4	-1.5
7	Birmingham-Hoover, AL	7.9	6.4	-1.5
8	Cincinnati-Middletown, OH-KY-IN	8.6	7.1	-1.5
9	Riverside-San Bernardino-Ontario, CA	13.6	12.1	-1.5
10	Nashville-Davidson–Murfreesboro–Franklin, TN	8.1	6.6	-1.5
11	Louisville/Jefferson County, KY-IN	9.7	8.3	-1.4
12	San Jose-Sunnyvale-Santa Clara, CA	10.0	8.6	-1.4
13	Columbus, OH	7.5	6.1	-1.4
14	Kansas City, MO-KS	8.0	6.6	-1.4
15	Houston-Sugar Land-Baytown, TX	8.1	6.8	-1.3
16	Charlotte-Gastonia-Rock Hill, NC-SC	10.8	9.5	-1.3
17	Seattle-Tacoma-Bellevue, WA	8.7	7.4	-1.3
18	San Francisco-Oakland-Fremont, CA	9.4	8.1	-1.3
19	Los Angeles-Long Beach-Santa Ana, CA	11.4	10.1	-1.3
20	Salt Lake City, UT	6.7	5.5	-1.2
21	Phoenix-Mesa-Glendale, AZ	8.5	7.3	-1.2
22	St. Louis, MO-IL	8.8	7.6	-1.2
23	Dallas-Fort Worth-Arlington, TX	7.8	6.7	-1.1
24	San Diego-Carlsbad-San Marcos, CA	10.0	8.9	-1.1
25	Detroit-Warren-Livonia, MI	11.6	10.5	-1.1
26	Portland-Vancouver-Hillsboro, OR-WA	9.3	8.2	-1.1
27	Atlanta-Sandy Springs-Marietta, GA	9.8	8.8	-1.0
28	Austin-Round Rock-San Marcos, TX	6.8	5.8	-1.0
29	San Antonio-New Braunfels, TX	7.5	6.5	-1.0
30	Memphis, TN-MS-AR	10.0	9.0	-1.0
31	Chicago-Joliet-Naperville, IL-IN-WI	9.8	8.9	-0.9
32	Minneapolis-St. Paul-Bloomington, MN-WI	6.3	5.5	-0.8
33	Raleigh-Cary, NC	8.5	7.7	-0.8
34	Providence-Fall River-Warwick, RI-MA – Metro	11.1	10.3	-0.8
35	Richmond, VA	7.1	6.4	-0.7
36	New Orleans-Metairie-Kenner, LA	7.2	6.5	-0.7
37	Cleveland-Elyria-Mentor, OH	7.8	7.1	-0.7
38	Oklahoma City, OK	5.5	4.8	-0.7
39	Denver-Aurora-Broomfield, CO	8.6	7.9	-0.7
40	Hartford-West Hartford-East Hartford, CT – Metro	9.0	8.4	-0.6
41	Milwaukee-Waukesha-West Allis, WI	8.0	7.4	-0.6
42	Indianapolis-Carmel, IN	8.4	7.8	-0.6
43	Baltimore-Towson, MD	7.7	7.2	-0.5
44	Virginia Beach-Norfolk-Newport News, VA-NC	7.1	6.6	-0.5
45	Boston-Cambridge-Quincy, MA-NH – Metro	6.6	6.1	-0.5
46	Washington-Arlington-Alexandria, DC-VA-MD-WV	6.0	5.6	-0.4
47	Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	8.6	8.6	0.0
48	Pittsburgh, PA	7.2	7.2	0.0
49	New York-Northern New Jersey-Long Island, NY-NJ-PA	8.6	8.8	0.2
50	Rochester, NY	7.8	8.1	0.3
51	Buffalo-Niagara Falls, NY	8.1	8.5	0.4

Infographic: Growth of All Occupations by Industry & Education, 2001-2011

Rob Sentz — Fri, 04 May 2012 09:45:18 -0400

We recently partnered with Catherine Mulbrandon at VisualizingEconomics.com to create a series of treemaps that illustrate important aspects of the labor market. In this post we provide a sneak peek at two of the graphics she created. The remainder will be posted in An Illustrated Guide to Income in the United States, a booklet from Catherine set to be released this summer.

These two graphics are based on EMSI’s labor market database, which is a combination of over 80 public and private data sources. More specifically, the first table shows job change for all occupations by industry (based on 2-digit supersectors, as defined by the North America Industry Classification System) and the second shows occupation change by education level. The data is from 2001-2011.

Red indicates decline and blue indicates growth.

Each square on the graphic indicates a specific 5-digit occupation classified by the Standard Occupational Classification system. There are over 800 unique squares present on the charts. Large squares, like the ones on the upper right and in the retail trade sector, indicate a lot of jobs for the specific occupation code. Smaller squares indicate occupations with less jobs.

In the graphic above we have pulled together occupation data related to all 20 NAICS supersectors. Government, health care, and retail trade have the largest employment. Utilities, mining, and management of companies have the fewest jobs. Also note the size of the squares within each industry sector. Here are a few observations:

Broad momentum. It is interesting to note how each broad industry sector tended to either be dominated by growth or decline. For instance, with very few exceptions, almost every occupation within the manufacturing sector declined from 2001-2011. The same holds true for construction, information, agriculture, and, to a certain extent, retail trade. Conversely, sectors like health care, educational services, professional/scientific/technical services, accommodation and even arts tended to show occupational growth.
Mixed sectors. Other industry sectors like finance, administrative, real estate, wholesale trade, and government were much more mixed.

The graphic above shows the distribution of jobs across all levels of educational attainment. We use the same 5-digit SOC codes and group them according to what their typical educational attainment is. Where possible, occupation titles are included so you can get a sense of where certain jobs fall. Here are a few quick observations:

The OJT sectors (on-the-job training) are huge. This includes short-term OJT (lower right), moderate-term OJT (upper left), long-term OJT (middle right), and work experience in a related field (center). Also notice how the occupations in these sectors are less stable than the others. This is consistent with what was observed in the latest recession — jobs with higher education levels tend to perform better in tough economic times.
Advanced degrees showed growth. Over the past 10 years, every occupation associated with a more advanced degree (master’s, doctoral, professional) showed some sort of growth.
The other sectors have mixed results. Bachelor’s degrees showed more stability over the past 10 years, but there are a handful of occupations that declined since 2001. The same holds true for associate’s, postsecondary vocational awards, and degrees plus work experience.

Metro Job Recovery in 2011

Aaron M. Renn — Thu, 15 Mar 2012 17:13:38 -0400

The latest BLS release for metro area unemployment has full year averages for 2011 available, so we can see which cities added the most jobs last year. On the whole, it was a much better year for metros than we’ve seen in the recent past. The national economy added jobs, and all but two large metros did as well. New York City added the most jobs of any region, but given that it is far and away the biggest city in America, it should do so. NYC ranked only the middle of the pack on a percentage growth basis. On that measure, Austin, Texas was number one.

The top percentage gainer in the Midwest region? Detroit, Michigan. Perhaps this shouldn’t be surprising either, as manufacturing is pro-cyclical.

Here is the performance of the metro areas in the United States with more than one million people, ranked by percentage change. The data is also available in spreadsheet form.

Rank	Metro Area	2010	2011	Total Change	Pct Change
1	Austin-Round Rock-San Marcos, TX	769.5	791.4	21.9	2.85%
2	San Jose-Sunnyvale-Santa Clara, CA	855.2	878.2	23.0	2.69%
3	Houston-Sugar Land-Baytown, TX	2528.1	2593.1	65.0	2.57%
4	Charlotte-Gastonia-Rock Hill, NC-SC	807.5	826.7	19.2	2.38%
5	Nashville-Davidson–Murfreesboro–Franklin, TN	734.3	751.7	17.4	2.37%
6	Salt Lake City, UT	608.1	622.0	13.9	2.29%
7	Detroit-Warren-Livonia, MI	1737.1	1775.3	38.2	2.20%
8	Dallas-Fort Worth-Arlington, TX	2860.9	2921.7	60.8	2.13%
9	Raleigh-Cary, NC	498.1	508.6	10.5	2.11%
10	Pittsburgh, PA	1125.3	1148.6	23.3	2.07%
11	Oklahoma City, OK	558.5	569.6	11.1	1.99%
12	Tampa-St. Petersburg-Clearwater, FL	1112.0	1132.3	20.3	1.83%
13	Portland-Vancouver-Hillsboro, OR-WA	968.8	986.1	17.3	1.79%
14	Minneapolis-St. Paul-Bloomington, MN-WI	1697.1	1727.1	30.0	1.77%
15	Baltimore-Towson, MD	1274.0	1293.5	19.5	1.53%
16	Seattle-Tacoma-Bellevue, WA	1641.2	1666.1	24.9	1.52%
17	Denver-Aurora-Broomfield, CO	1193.5	1211.6	18.1	1.52%
18	Columbus, OH	903.3	916.9	13.6	1.51%
19	Miami-Fort Lauderdale-Pompano Beach, FL	2185.6	2218.3	32.7	1.50%
20	Phoenix-Mesa-Glendale, AZ	1688.9	1712.8	23.9	1.42%
21	Atlanta-Sandy Springs-Marietta, GA	2272.6	2302.9	30.3	1.33%
22	New Orleans-Metairie-Kenner, LA	519.1	526.0	6.9	1.33%
23	San Antonio-New Braunfels, TX	843.0	853.2	10.2	1.21%
24	Richmond, VA	602.4	609.5	7.1	1.18%
25	New York-Northern New Jersey-Long Island, NY-NJ-PA	8306.8	8403.9	97.1	1.17%
26	Indianapolis-Carmel, IN	871.1	881.2	10.1	1.16%
27	Jacksonville, FL	583.1	589.6	6.5	1.11%
28	Rochester, NY	503.1	508.7	5.6	1.11%
29	Washington-Arlington-Alexandria, DC-VA-MD-WV	2962.9	2995.5	32.6	1.10%
30	Hartford-West Hartford-East Hartford, CT – Metro	533.2	538.9	5.7	1.07%
31	Chicago-Joliet-Naperville, IL-IN-WI	4246.6	4291.4	44.8	1.05%
32	Milwaukee-Waukesha-West Allis, WI	805.8	814.1	8.3	1.03%
33	Louisville/Jefferson County, KY-IN	592.9	599.0	6.1	1.03%
34	Kansas City, MO-KS	971.6	981.4	9.8	1.01%
35	Orlando-Kissimmee-Sanford, FL	1001.1	1011.0	9.9	0.99%
36	Memphis, TN-MS-AR	589.8	595.4	5.6	0.95%
37	Cincinnati-Middletown, OH-KY-IN	980.8	989.4	8.6	0.88%
38	Buffalo-Niagara Falls, NY	538.2	542.7	4.5	0.84%
39	San Francisco-Oakland-Fremont, CA	1880.2	1894.3	14.1	0.75%
40	Boston-Cambridge-Quincy, MA-NH – Metro	2426.5	2443.3	16.8	0.69%
41	Los Angeles-Long Beach-Santa Ana, CA	5126.8	5162.2	35.4	0.69%
42	San Diego-Carlsbad-San Marcos, CA	1222.8	1231.2	8.4	0.69%
43	St. Louis, MO-IL	1286.9	1295.4	8.5	0.66%
44	Las Vegas-Paradise, NV	803.6	808.3	4.7	0.58%
45	Riverside-San Bernardino-Ontario, CA	1125.9	1129.7	3.8	0.34%
46	Philadelphia-Camden-Wilmington, PA-NJ-DE-MD	2697.0	2705.9	8.9	0.33%
47	Providence-Fall River-Warwick, RI-MA – Metro	541.3	542.8	1.5	0.28%
48	Virginia Beach-Norfolk-Newport News, VA-NC	735.2	736.8	1.6	0.22%
49	Cleveland-Elyria-Mentor, OH	991.1	992.7	1.6	0.16%
50	Birmingham-Hoover, AL	489.5	488.6	-0.9	-0.18%
51	Sacramento–Arden-Arcade–Roseville, CA	809.9	802.0	-7.9	-0.98%

This first appeared at Aaron's blog, Urbanophile.com.

Urban Densities Exclude Rural Areas: Avent Postscript

Wendell Cox — Thu, 08 Sep 2011 01:09:51 -0400

We recently noted that Ryan Avent was one third right in his recent Sunday New York Times article on urban density. Avent has posted a response suggesting that it is inappropriate to use average urban densities in urban productivity analyses, as we had done, but that "weighted average densities" should be used instead. Weighted average density was not mentioned in his New York Times article.

In the interim, we were able to find the studies on urban density and productivity that seem to match those Avent refers to in his New York Times article. There are two studies concluding that doubling employment (not population) density increases productivity by six percent (Ciccone & Hall, 1996 and Harris & Ioannides, 2000), as Avent noted. Another study (Davis, Fisher & Whited, 2007) indicates that doubling employment densities could increase productivity by as much as 28 percent, also as Avent noted.

Urban and Rural Density Combined Are Not Urban Density: In contrast to Avent's preference for weighted average density, each of the studies uses average density, like with our analysis. More importantly the econometric formulas in the studies do not include an urban density variable. The density variables in all three studies include rural areas.

The studies use county, metropolitan area and sub-metropolitan area densities, each of which contain far more rural land than urban land. By definition, urban areas exclude rural areas and, as a result, the moment rural areas become a part of the calculation, the result cannot be urban densities. In 2000, Census Bureau data showed counties (county equivalent level jurisdictions), which comprise the entire nation, to be less than three percent urban and more than 97 percent rural (Figure 1). Metropolitan areas also have a similar predominance of rural land (Figure 1). Among major metropolitan areas (those with more than 1,000,000 population) in 2000, approximately 85 percent of the land was rural and 15 percent of the land was urban (Figure 2).

Ciccone & Hall use employment density at the county level and thus mix urban and rural densities. Harris & Ioannides use employment densities at the metropolitan statistical area or the primary metropolitan statistical area level (a sub-metropolitan designation since replaced by the more appropriately titled "metropolitan division"). Davis, Fisher & Whited use employment densities at the metropolitan statistical area level. The two studies using metropolitan areas or parts of metropolitan areas also mix urban and rural densities.

Urban Area Densities: Urban density is calculated at the urban area level, which is the area of continuous urban development. This is also called the urban footprint, which is generally indicated by the lights of the city one would see from an airplane on a clear night. Urban areas are delineated using the smallest census geographical units ("census blocks," which are smaller than census tracts) each ten years. The 2010 data will be released next year. Among urban areas, the highest density core urban area in a major metropolitan area (Los Angeles) is approximately four times the lowest (Birmingham).

Nonsensical Metropolitan Area Densities: Theoretically, metropolitan areas are labor market areas, which include a core urban area (and sometimes more than one urban area) and nearby rural areas from which people commute to work in the urban area (can be called the "commuter shed"). However, in the United States, metropolitan areas are too coarsely defined for density comparisons with one another. US metropolitan areas are composed of complete counties or, in the six New England states, complete towns. This jurisdictionally based criteria can produce metropolitan areas that are much larger than genuine labor markets in a number of cases and some that are smaller. American metropolitan areas are not spatially consistent by any functional labor market definition. Metropolitan densities are thus nonsensical, no matter what density is being measured (such as population or employment density). Among major metropolitan areas, the highest density metropolitan area (New York) is 24 times that of the lowest density (Salt Lake City), six times the maximum difference in urban area density.

Metropolitan Ireland and Happenstance: In the similarly sized San Francisco (as used by Davis, Fisher and Whited) and Riverside-San Bernardino metropolitan areas, San Francisco has 1,700 square miles of rural land, while Riverside-San Bernardino has 26,000, approximately 15 times as much. At more than 27,000 square miles, Riverside-San Bernardino covers more land area than the Republic of Ireland. The difference in population densities between metropolitan areas is determined in considerable measure by the size (land area) of the included counties, not by the number of people in cities.

If the state of California were to carve out a new county composed of western Riverside and San Bernardino counties (as Colorado created Bloomfield County in the early 2000s), the land area of the metropolitan area could be reduced 95 percent, because the remainder would not meet the criteria for inclusion in Riverside-San Bernardino. The importance of the density variable for Riverside-San Bernardino in econometric formulas would be increased many times. With only 3,100 county level jurisdictions of varying sizes, this kind of incomparability cannot help but occur. The boundaries of metropolitan areas are defined by political happenstance.

On the other hand, the nation's urban areas are built up from 7,000,000 census blocks. This permits a fine grained definition that makes urban areas appropriate for density comparisons. The definition of urban areas is beyond political fiat.

Metropolitan areas in the United States could be readily defined at the census block level, just like urban areas. Regrettably, the Office of Management and Budget missed another opportunity in the 2010 census to make the necessary criteria change. U.S. metropolitan area data is of great value for most analysis, but misleading for spatial or density analysis.

Low-Density Productivity: Subregionalizing the density and productivity analysis would pose problems. Avent uses household incomes as his standard (and we agree that cost of living differentials are important). The San Jose metropolitan area has the highest household incomes of any major metropolitan area and would therefore be among the most productive. Yet, San Jose's automobile-oriented Silicon Valley, to which much of the productivity is attributable, has a far lower employment density than the transit and pedestrian oriented cores of Manhattan and San Francisco (and yes, even not-so-transit oriented downtown Phoenix). In low-density Seattle, Microsoft's automobile oriented Redmond campus probably ranks among the most productive real estate in the country, yet its employment density (like that of Silicon Valley) pales by comparison to the higher density cores of Seattle, Phoenix, Nashville, Oklahoma City and virtually every other downtown core of a major metropolitan area.

At the End, Agreement: Avent concludes, "I just want to make sure we stop costing ourselves easy opportunities for growth." I could not agree more. It is time to abandon regulations that artificially raise housing prices, deprive households of a better standard of living, and drive them to places they would rather not live. For centuries, people have flocked to urban areas for better economic opportunities. Urban areas should be places where people can realize their aspirations, not places that repel them because it doesn't suit the interests of those already there.